Maximum A Posteriori probability estimate (MAP)

Find the Highest Maximum A Posteriori probability estimate (MAP) of a posterior, i.e., the value associated with the highest probability density (the "peak" of the posterior distribution). In other words, it is an estimation of the mode for continuous parameters. Note that this function relies on estimate_density, which by default uses a different smoothing bandwidth ("SJ") compared to the legacy default implemented the base R density function ("nrd0").

Arguments

Density estimation method. Can be "kernel" (default), "logspline" or "KernSmooth".

...

Currently not used.

effects

Should results for fixed effects, random effects or both be returned?
Only applies to mixed models. May be abbreviated.

parameters

Regular expression pattern that describes the parameters that
should be returned. Meta-parameters (like lp__ or prior_) are
filtered by default, so only parameters that typically appear in the
summary() are returned. Use parameters to select specific parameters
for the output.

component

Should results for all parameters, parameters for the conditional model
or the zero-inflated part of the model be returned? May be abbreviated. Only
applies to brms-models.

Value

A numeric value if posterior is a vector. If posterior
is a model-object, returns a data frame with following columns:

Parameter The model parameter(s), if x is a model-object. If x is a vector, this column is missing.

MAP_Estimate The MAP estimate for the posterior or each model parameter.